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Breast Cancer Wisconsin (Diagnostic) Dataset

Breast Cancer Wisconsin (Diagnostic) Dataset

In this article, we compare a number of classification methods for the breast cancer dataset. The details regarding this dataset can be found in Diagnostic Wisconsin Breast Cancer Database [1]. We would use the following classification methods and then compare them in terms of performance.

Dataset

As can be seen, the number of instances is 569 and the number of attributes is 32. The object of the exercise is to create a classification model that can classify the type of Diagnosis base on the rest of the attributes. However, first, let's plot a count plot for Diagnosis attribute.

X and y sets

Features with high variance

Moreover, high variance for some features can hurt our modeling process. For this reason, we would like to standardize features by removing the mean and scaling to unit variance.

Training and testing sets

StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

Modeling: Random Forest Classifier

A random forest classifier (RFC) fits several decision tree classifiers on (using sub-samples of the dataset) and then averages them to improve the predictive accuracy. See sklearn.ensemble.RandomForestClassifier for more details.

Some of the metrics that we use here to mesure the accuracy: \begin{align} \text{Confusion Matrix} = \begin{bmatrix}T_p & F_p\\ F_n & T_n\end{bmatrix}. \end{align}

where $T_p$, $T_n$, $F_p$, and $F_n$ represent true positive, true negative, false positive, and false negative, respectively.

\begin{align} \text{Precision} &= \frac{T_{p}}{T_{p} + F_{p}},\\ \text{Recall} &= \frac{T_{p}}{T_{p} + F_{n}},\\ \text{F1} &= \frac{2 \times \text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}\\ \text{Balanced-Accuracy (bACC)} &= \frac{1}{2}\left( \frac{T_{p}}{T_{p} + F_{n}} + \frac{T_{n}}{T_{n} + F_{p}}\right ) \end{align}

The accuracy can be a misleading metric for imbalanced data sets. In these cases, a balanced accuracy (bACC) [4] is recommended that normalizes true positive and true negative predictions by the number of positive and negative samples, respectively, and divides their sum by two.

Random Forest Classifier with Default Parameters

Random Forest Classifier with the Best Parameters

In order to find the parameters for our model, we can sue RandomizedSearchCV. Here, we have defined a function Best_Parm to find the best parameters.

Since we have identified the best parameters for our modeling, we train another model using these parameters.

References

  1. UC Irvine Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic) Data Set
  2. scikit-learn Random Forest Classifier
  3. Random Forest Classifier Wikipedia page
  4. Mower, Jeffrey P. "PREP-Mt: predictive RNA editor for plant mitochondrial genes." BMC bioinformatics 6.1 (2005): 1-15.